Book Image

Modern Big Data Processing with Hadoop

By : V Naresh Kumar, Manoj R Patil, Prashant Shindgikar
Book Image

Modern Big Data Processing with Hadoop

By: V Naresh Kumar, Manoj R Patil, Prashant Shindgikar

Overview of this book

The complex structure of data these days requires sophisticated solutions for data transformation, to make the information more accessible to the users.This book empowers you to build such solutions with relative ease with the help of Apache Hadoop, along with a host of other Big Data tools. This book will give you a complete understanding of the data lifecycle management with Hadoop, followed by modeling of structured and unstructured data in Hadoop. It will also show you how to design real-time streaming pipelines by leveraging tools such as Apache Spark, and build efficient enterprise search solutions using Elasticsearch. You will learn to build enterprise-grade analytics solutions on Hadoop, and how to visualize your data using tools such as Apache Superset. This book also covers techniques for deploying your Big Data solutions on the cloud Apache Ambari, as well as expert techniques for managing and administering your Hadoop cluster. By the end of this book, you will have all the knowledge you need to build expert Big Data systems.
Table of Contents (12 chapters)

Understanding data structure principles

Let's go through some important data architecture principles:

  • Data is an asset to an enterprise: Data has a measurable value. It provides some real value to the enterprise. In modern times, data is treated like real gold.
  • Data is shared enterprise-wide: Data is captured only once and then used and analyzed many times. Multiple users access the same data for different uses cases and requirements.
  • Data governance: Data is governed to ensure data quality.
  • Data management: Data needs to be managed to attain enterprise objectives.
  • Data access: All users should have access to data.
  • Data security: Data should be properly secured and protected.
  • Data definition: Each attribute of the data needs to be consistently defined enterprise-wide.

Now that we know the basics of big data and its principles, let's get into some real action.